# ReasonSTL: Bridging Natural Language and Signal Temporal Logic via Tool-Augmented Process-Rewarded Learning

arXiv: https://arxiv.org/abs/2605.06483 · HTML: https://mlsanity.com/paper/2605.06483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 7 May 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 13 of 13)

- [nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models](https://mlsanity.com/paper/2303.04864/) (2023)
- [NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models](https://mlsanity.com/paper/2305.07766/) (2023)
- [Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation](https://mlsanity.com/paper/2305.15541/) (2023)
- [Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification](https://mlsanity.com/paper/2303.08006/) (2023)
- [DeepSTL -- From English Requirements to Signal Temporal Logic](https://mlsanity.com/paper/2109.10294/) (2021)
- [Formal Synthesis of Controllers for Safety-Critical Autonomous Systems: Developments and Challenges](https://mlsanity.com/paper/2402.13075/) (2024)
- [Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge](https://mlsanity.com/paper/2505.20658/) (2025)
- [Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning](https://mlsanity.com/paper/2212.03181/) (2022)
- [Zero-Shot Trajectory Planning for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2501.13457/) (2025)
- [TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2510.00225/) (2025)
- [Trust-Aware Motion Planning for Human-Robot Collaboration under Distribution Temporal Logic Specifications](https://mlsanity.com/paper/2310.01163/) (2023)
- [Power Line Inspection Tasks with Multi-Aerial Robot Systems via Signal Temporal Logic Specifications](https://mlsanity.com/paper/2302.04691/) (2023)
- [Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks](https://mlsanity.com/paper/2509.12813/) (2025)

## Built on by (most cited first, 1 of 1)

- [Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation](https://mlsanity.com/paper/2609.33287/) (2026)

## Abstract (arXiv)

Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has been widely used for the verification and synthesis of autonomous systems and cyber-physical systems. In practice, however, users often express their requirements in natural language rather than in structured STL formulas, making natural-language-to-STL translation a critical yet challenging task. Manual specification requires temporal-logic expertise and cannot scale, while prompting commercial LLM APIs incurs substantial token costs and may expose sensitive system requirements to third-party services, raising privacy concerns for industrial deployment. To address these challenges, we present textscReasonSTL, a tool-augmented framework that adapts local open-source language models for natural-language-to-STL generation. textscReasonSTL decomposes the translation process into explicit reasoning, deterministic tool calls, and structured formula construction. We further introduce process-rewarded training to supervise both tool-use trajectories and final formulas, together with textscSTL-Bench, a bilingual, computation-aware benchmark grounded in real-world signals. Experiments show that a 4B model trained with textscReasonSTL achieves state-of-the-art performance in both automatic metrics and human evaluations, demonstrating that textscReasonSTL provides a transparent, low-cost, and privacy-preserving alternative for formal specification drafting.